David Weiss

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 8 papers; trusts 189 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it; #3,218 of 35,345 by trust here, #3,998 by citations
• Natural Language Processing — 143 citations into it; #1,153 of 5,688 by trust here, #1,432 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #20,920 of 21,575 by trust here, #6,671 by citations
• Model Architectures — 32 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Molecular Biology — 22 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Information Extraction — 13 citations into it

Whom David Weiss trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. L. Eon Bottou
3. Joakim Nivre
4. Michael Collins
5. Christopher D. Manning
6. Yoshua Bengio
7. Alex Krizhevsky
8. Danqi Chen
9. Chris Dyer
10. Ilya Sutskever

Who trusts David Weiss overall

1. Emma Strubell
2. Yoav Goldberg
3. Soravit Changpinyo
4. Hyun Kim
5. Kevin Clark
6. Swabha Swayamdipta
7. Eliyahu Kiperwasser
8. Miguel Ballesteros
9. Jan Buys
10. Chris Dyer

Papers from David Weiss's perspective

Ordered by how strongly the researchers David Weiss trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Globally Normalized Transition-Based Neural Networks (2016) — 577 citations
• Structured Training for Neural Network Transition-Based Parsing (2015) — 236 citations
• Linguistically-Informed Self-Attention for Semantic Role Labeling (2018) — 401 citations
• Stack-propagation: Improved Representation Learning for Syntax (2016) — 87 citations
• Dependency Parsing (2009) — 624 citations
• Learning Cross-Context Entity Representations from Text (2020) — 33 citations
• Natural Language Processing with Small Feed-Forward Networks (2017) — 39 citations
• A Fast, Compact, Accurate Model for Language Identification of Codemixed Text (2018) — 36 citations